Development Of Optimization Model Research Articles

Parametric study of FSW process on AA6061-Activated carbon composite using particle swarm optimization

Now a days concerning about a metal matrix composite is welded by Friction Stir welding (FSW) techniques and also FSW parameter study method is optimised Particle Swarm optimisation (PSO) technique. The developed material was developed by modified down pouring stir casting technique with matrix as aluminium alloy and reinforcement as activated carbon. Using the response surface methodology of composite is assessed by mechanical and morphological evaluations with their regression expressions. The experimental process is planned on developed composite with connection parameters like spindle speed, axial load, tool pin, and materials combination. In experiment the standard of weld is indebted by tensile study and hardness of the composite. The developed optimization model is matured to optimize the four contribution input levels of welding process. From optimization results confirmative check is executed and results shows less error from experimental values.

A new supply chain design to solve supplier selection based on internet of things and delivery reliability

In this study, an attempt is made to examine the planning for supply, production, and distribution with an integrated approach to reduce waste and returned products in such a way that the supplier and manufacturer exchange information in supply chain. In this paper, manufacturer share information with suppliers through IoT and blockchain. For best implementation of supply chain based on these technologies, a manufacturer must chose the optimal combination of suppliers that can better meet the criteria of technologies implementation. In this paper, IoT and blockchain are examined to supply chain management by considering the impactful criteria of implementation in suppliers' selection and optimization. For this purpose, this paper applies the multi criteria decision making (MCDM) methods for prioritizing the suppliers. Then, the obtained weights are plugged into the model as the inputs of the designed model. In this study chance constraint programming (CCP) is leveraged to develop optimization models that take the uncertainties and randomness into account. Since the developed model is a non-differentiable and non-convex, genetic algorithm (GA) and particle swarm optimization (PSO) are leveraged to solve the formulated model. Finally, the efficiency of the presented method is verified through a case study from the food supply chain.

Sustainable planning of multipurpose hydropower reservoirs with environmental impacts in a simulation–optimization framework

Abstract Hydropower projects involve enormous investments that require an efficient cost–benefit framework and optimization model for proper development. Dams and hydropower plants have many impacts on the environment. These environmental impacts are often not included in the economic calculations and planning of the projects, which leads to the loss of natural resources. The primary purpose of this research is to incorporate environmental impacts into optimization and decision-making. A comprehensive simulation–optimization model is developed to optimize hydropower decisions. The positive and negative values of environmental impacts are incorporated into an economic objective function under different scenarios, and optimal design was done for each scenario. The results show that considering environmental economics affects the multipurpose hydropower project's NPV and decision outcomes. Considering environmental impacts compared to not considering them has reduced NPV of the project by 13.9%. The results emphasize the importance of including these impacts to achieve sustainable development and management.

Sustainable Building Optimization Model for Early-Stage Design

Buildings represent the largest potential for carbon reduction worldwide. This highlights the need for a simulation and optimization method for energy management. The early design stage of buildings represents an important phase in which choices can be made to optimize design parameters. These parameters can focus on multiple areas, including energy and thermal comfort. This paper introduces the optimization of early-stage sustainable building design considering end-user energy consumption. It proposes an optimization model that integrates multiple layers, which consist of a parametric energy simulation, artificial neural network, and genetic algorithm. The proposed optimization model considers a single objective function to obtain the optimal design. The targeted goal is to obtain minimal energy consumption for residential buildings during the early design stages. Key design parameters of the building were identified for optimization and feasible ranges for them were obtained using genetic algorithms. Finally, the results of this paper include the identification of the optimal building design for the thermal comfort analysis and optimal energy performance. The model was applied to a case study in Egypt and the results showed that using the developed optimization model can lead to a 25% reduction in energy consumption.

A stomatal optimization model adopting a conservative strategy in response to soil moisture stress

Leaf stomata play a central role in mediating the exchange of water vapor and carbon dioxide between the atmosphere and the terrestrial ecosystems. Stomatal response to the environment presumably optimizes the trade-off between carbon uptake and water loss. Recently developed optimization models show their remarkable success in capturing empirical patterns of stomata. However, solutions of these models with different mathematical details should vary for the same environments, indicating that stomatal strategies implied in these models may differ and require further investigation. The present study proposed a novel stomatal optimization model, assuming that stomatal regulation aims to achieve a water-carbon balance shaped by a hydraulic-based weight factor (HBW model). In particular, we introduce the balancing point (BP) for analyzing the behavioral difference between models. We tested the HBW model based on set scenarios and leaf-level gas exchange data and compared it with two existing models. Results show that these models differ mainly in stomatal sensitivity to soil moisture. BP characteristics reveal the behavioral difference between models, and only the HBW model, which is more sensitive to soil moisture, captures the actual response patterns of BP to water stress well. This work emphasizes the differences in stomatal strategies among different optimization models, and provides an alternative modeling approach, which offers a more conservative stomatal strategy that can stand as a useful comparison with existing models.

Rice Growth Stage Classification via RF-Based Machine Learning and Image Processing

Rice is one of the most significant crops cultivated in Asian countries. In Taiwan, almost half of the arable land is used for growing rice. The life cycle of paddy rice can be divided into several stages: vegetative stage, reproductive stage, and ripening stage. These three main stages can be divided into more detailed stages. However, the transitions between stages are challenging to observe and determine, so experience is required. Thus, rice cultivation is challenging for inexperienced growers, even with the standard of procedure (SOP) provided. Additionally, aging and labor issues have had an impact on agriculture. Furthermore, smart farming has been growing rapidly in recent years and has improved agriculture in many ways. To lower the entry requirements and help novices better understand, we proposed a random forest (RF)-based machine learning (ML) classification model for rice growth stages. The experimental setup installed in the experiment fields consists of an HD smart camera (Speed-dome) to collect the image and video data, along with other internet of things (IoT) devices such as 7-in-1 soil sensors, a weather monitoring station, flow meter, and milometer connected with LoRa base station for numerical data. Then, different image processing techniques such as object detection, object classification, instance segmentation, excess green index (EGI), and modified excess green index (EGI) were used to calculate the paddy height and canopy cover (CC) or green coverage (GC). The proposed ML model uses these values as input. Furthermore, growth-related factors such as height, CC, accumulative temperature, and DAT are used to develop our model. An agronomist has been consulted to label the collected different stages of data. The developed optimal model has achieved an accuracy of 0.98772, and a macro F1-score of 0.98653. Thus, the developed model produces high-performance accuracy and can be employed in real-world scenarios.

Rail train operation energy-saving optimization based on improved brute-force search

Rail train operation energy consumption mainly focuses on train traction energy consumption. Reducing train traction energy consumption in rail transit operation is significant to developing a green and low-carbon economy and reducing operation costs. The rail train operation energy-saving optimization framework is developed considering the utilization of regenerative braking energy. Firstly, three objectives of punctual arrival, fixed-point parking and minimum energy consumption are provided by train operation strategy analysis. Secondly, the improved brute-force search is developed to solve train operation energy-saving multi-objective problems. The running time, speed, distance, power, and energy consumption of operation intervals are calculated. Finally, Guangzhou Metro Line 7 is taken as an example to verify the effectiveness of the developed optimization model. The results show that the improved brute-force search method effectively finds a more energy-saving turning point under constant interval operation time and has a better energy-saving effect than two other heuristic algorithms.

Optimal configuration of hybrid energy systems considering power to hydrogen and electricity-price prediction: A two-stage multi-objective bi-level framework

This paper develops a two-stage multi-objective bi-level framework to optimize the sizing of a grid-connected electricity-hydrogen system. Firstly, a multi-objective bi-level capacity configuration optimization model considering the different functional orientations of hydrogen energy and electricity-price prediction is established. Then, to solve the above multi-objective bi-level model, a two-stage solution algorithm is proposed. In stage one, the CPLEX solver and non-dominated sorting genetic algorithm II are employed to obtain the solutions of the developed optimization model. In stage two, an entropy method is applied to get the importance of the three objectives of the outer model, whereas a cumulative prospect theory is used to rank the best Pareto solution. Finally, an industrial park in Aksai Kazak Autonomous County is chosen for case study, the results show: (1) the best capacity configuration alternative, which includes 22 wind turbines, 210 photovoltaic panels, 2 gas turbines, 2 fuel cells, 1 electrolyzer, and 3 hydrogen tanks, owns the NPB of 161,503 CNY, the ACE of 93,111 kg, and the LOEC of 603,874 kWh. (2) the ACE with the weight of 0.527 is the most important objective. (3) Sensitivity analysis on electricity price fluctuations of ±5% and ±10% presents that the proposed approach is robust.

Towards Fuel Consumption Reduction Based on the Optimum Contra-Rotating Propeller

This paper presents the effect of selecting a contra-rotating propeller (CRP) for a bulk carrier at the engine operating point with minimum fuel consumption, as well as ensuring the safety of the propeller in terms of cavitation and noise. Using a developed optimization model, the geometry of a CRP was selected for different propeller diameters, the same propeller diameter as that of a fixed pitch propeller (FPP) installed on the bulk carrier, and at 90% of the FPP diameter. Additionally, each case was optimized with both no-cup and heavy-cup configurations. In general, the CRP showed better performance than the FPP in terms of efficiency, cavitation, and fuel economy. At the same time, the level of performance was increased when considering the CRP cupping percentage. It was concluded that the CRP can achieve a gain in fuel economy of up to 6.2% in a no-cup configuration when compared to an FPP, and up to 11.7% with a cupped configuration.

Aircraft route optimization with simulated annealing for a mixed airspace composed of free and fixed route structures

PurposeThe purpose of this paper is to create a flight route optimization for all flights that aims to minimize the total cost consists of fuel cost, ground delay cost and air delay cost over the fixed route and free route airspaces.Design/methodology/approachEfficient usage of current available airspace capacity becomes more and more important with the increasing flight demands. The efficient capacity usage of an airspace is generally in contradiction to optimum flight efficiency of a single flight. It can only be achieved with the holistic approach that focusing all flights over mixed airspaces and their routes instead of single flight route optimization for a single airspace. In the scope of this paper, optimization methods were developed to find the best route planning for all flights considering the benefits of all flights not only a single flight. This paper is searching for an optimization to reduce the total cost for all flights in mixed airspaces. With the developed optimization models, the determination of conflict-free optimum routes and delay amounts was achieved with airway capacity and separation minimum constraints in mixed airspaces. The mathematical model and the simulated annealing method were developed for these purposes.FindingsThe total cost values for flights were minimized by both developed mathematical model and simulated annealing algorithm. With the mathematical model, a reduction in total route length of 4.13% and a reduction in fuel consumption of 3.95% was achieved in a mixed airspace. The optimization algorithm with simulated annealing has also 3.11% flight distance saving and 3.03% fuel consumption enhancement.Research limitations/implicationsAlthough the wind condition can change the fuel consumption and flight durations, the paper does not include the wind condition effects. If the wind condition effect is considered, the shortest route may not always cause the least fuel consumption especially under the head wind condition.Practical implicationsThe results of this paper show that a flight route optimization as a holistic approach considering the all flight demand information enhances the fuel consumption and flight duration. Because of this reason, the developed optimization model can be effectively used to minimize the fuel consumption and reduce the exhaust emissions of aircraft.Originality/valueThis paper develops the mathematical model and simulated annealing algorithm for the optimization of flight route over the mixed airspaces that compose of fixed and free route airspaces. Each model offers the best available and conflict-free route plan and if necessary required delay amounts for each demanded flight under the airspace capacity, airspace route structure and used separation minimum for each airspace.

SIRTEM: Spatially Informed Rapid Testing for Epidemic Modeling and Response to COVID-19

COVID-19 outbreak was declared a pandemic by the World Health Organization on March 11, 2020. To minimize casualties and the impact on the economy, various mitigation measures have being employed with the purpose to slow the spread of the infection, such as complete lockdown, social distancing, and random testing. The key contribution of this article is twofold. First, we present a novel extended spatially informed epidemic model, SIRTEM, Spatially Informed Rapid Testing for Epidemic Modeling and Response to COVID-19 , that integrates a multi-modal testing strategy considering test accuracies. Our second contribution is an optimization model to provide a cost-effective testing strategy when multiple test types are available. The developed optimization model incorporates realistic spatially based constraints, such as testing capacity and hospital bed limitation as well.

A Novel Stochastic Mixed-Integer-Linear-Logical Programming Model for Optimal Coordination of Hybrid Storage Systems in Isolated Microgrids Considering Demand Response

Storage systems and demand-response programs will play a vital role in future energy systems. Batteries, hydrogen or pumped hydro storage systems can be combined to form hybrid storage facilities to not only manage the intermittent behavior of renewable sources, but also to store surplus renewable energy in a practice known as ‘green’ storage. On the other hand, demand-response programs are devoted to encouraging a more active participation of consumers by pursuing a more efficient operation of the system. In this context, proper scheduling tools able to coordinate different storage systems and demand-response programs are essential. This paper presents a stochastic mixed-integer-lineal-logical framework for optimal scheduling of isolated microgrids. In contrast to other works, the present model includes a logical-based formulation to explicitly coordinate batteries and pumped hydro storage units. A case study on a benchmark isolated microgrid serves to validate the developed optimization model and analyze the effect of applying demand-response premises in microgrid operation. The results demonstrate the usefulness of the developed method, and it is found that operation cost and fuel consumption can be reduced by ~38% and ~82% by applying demand-response initiatives.

Maximum Power Tracking for Magnetic Field Editing-Based Omnidirectional Wireless Power Transfer

This paper proposes and implements an optimized omnidirectional wireless power transfer (WPT) system using distributed transmitter coils is proposed. Under the consideration of practical WPT applications which involve human safety concerns, such as daily home apparatus or wireless rechargeable medical implants, the proposed system can achieve the shortest charging time within the safe limits of human exposure to radiofrequency electromagnetic energy. Based on the calculation of the magnetic intensity generated by multi-coupling WPT systems, a more processable magnetic threshold model is presented and analyzed based on the common standard. A newly developed optimization model of the transmitter currents and accordant control strategy is designed and presented to realize both maximum power tracking and flux suppression at target positions. The double-resonant circuit topology with LCC compensation is adopted to reach the stable and synchronized transmitter currents. The cross-coupling problem in multi-input WPT systems will be solved as well. Full-range magnitude control is realized with a single controller and direct current power source. The hybrid frequency pacing technique is adopted for lower switching frequency and higher system efficiency under weak coupling operations. Taking practical wireless rechargeable medical implant as the research object, both simulation and experimental results validate the feasibility and the prospect of the proposed system.

CAPITAL MANAGEMENT IN THE CONTEXT OF ENSURING THE FINANCIAL SECURITY OF THE ENTERPRISE

The article considers certain aspects of enterprise capital management from the standpoint of ensuring the financial security of the business entity. It has been determined that the capital structure of the enterprise is one of the main factors that determines financial security. It has been established that there is no universal capital structure of enterprises, it is determined by the field of activity, industry specifics and the chosen strategy of the enterprise's development.
 Taking into account the presence of a significant proportion of borrowed funds in the capital structure of many domestic enterprises, the question of the need to ensure their financial security arises acutely.
 The scheme of capital management is considered as its two components: formation and use. The most common models in the world for justifying the capital structure of an enterprise, which are used in the process of capital management, are presented.
 The article analyzes existing approaches to determining the essence of the economic category "financial security of the enterprise" and existing approaches to its assessment. Despite the existence of a sufficient number of scientific works of domestic and foreign scientists devoted to the problems of ensuring the financial security of economic entities, the issue of the existence of a single universal evaluation criterion has not been resolved.
 A scientific approach to capital management based on the determination of its optimal structure, which would guarantee the financial security of the enterprise, is substantiated. The realization of this approach is ensured thanks to the built optimization economic-mathematical model. A feature of this model is its two-criteria nature. Minimization of its weighted average cost (WACC) was adopted as a criterion for optimizing the capital structure, and maximization of its profitability was accepted as a criterion for the efficiency of capital growth. The task of ensuring financial security is solved by introducing additional restrictions on the limit values of liquidity and financial stability indicators into the model. The problem of bicriteria is proposed to be solved by using the multi-criteria Pareto method, which is used in problems for which it is impossible to construct an extremum, but it is possible to propose the concept of a situation that does not deteriorate ("Pareto optimum"). The solution to the problem of optimizing the capital structure of the enterprise based on finding a compromise between the values of the "weighted average cost of capital" and "return on capital" indicators lies in the area of the Pareto set, but at the same time, this set is not convex, which makes it difficult to solve. To obtain a single optimal option for solving a two-criterion problem, it is proposed to use the method of M. D. Andrieieva and D.G. Khoroshykh.
 The use of the developed optimization model with appropriate restrictions on the limit values of liquidityindicators and financial stability allows solving the task of effective capital management while simultaneously ensuring the financial security of the enterprise.
 The difference between the proposed capital management approach of the enterprise and the existing ones is the possibility to achieve the optimal capital structure of the economic entity under the condition of simultaneously ensuring its financial security according to several criteria without the need to define a single criterion of financial security or apply a local one without taking into account other existing criteria for assessing financial security.
 The scientific novelty of the article lies in the further development of the capital management methodology of enterprises in the context of ensuring their financial security.

A water-energy-food-carbon nexus optimization model for sustainable agricultural development in the Yellow River Basin under uncertainty

Water scarcity, limited land resources and global warming are the most challenging issues facing sustainable agricultural development, and modern agriculture requires efficient and environmentally friendly agricultural water and land management practices. This paper proposes a water-energy-food-carbon nexus system optimization model, which aims to formulate a scientific and rational water and land resource allocation strategy for sustainable agricultural development to increase irrigation water productivity, reduce carbon emissions and enhance regional agriculture competitiveness. First, the carbon footprint life cycle assessment method was used to measure carbon sinks and carbon emissions across the ecosystem. Second, a multiobjective nonlinear programming model with the goal of maximizing irrigation water productivity, minimizing carbon emissions, and maximizing the competitiveness of low-carbon agriculture is established. Finally, an empirical study is performed in the Yellow River Basin, and an elitist nondominated sorting genetic algorithm is used to solve the model. The research results show that the water-energy-food-carbon nexus system optimization model based on uncertain conditions established in this paper, can maximize resource utilization efficiency and effectively measure the impact of regional agricultural production on the environment. It has reference significance for the realization of carbon neutrality in the region.

Community structure recovery optimization for partial disruption, functionality, and restoration in interdependent networks

Community structures exist in different infrastructure networks such as water, gas, and power networks among others, where each network is split into multiple sets of components. Such sets are sparsely connected but have densely connected components within each one of them. However, critical networks often depend on each other to function properly. Consequently, such interdependencies can both improve efficiency of the collection of networks but also increase their vulnerability to a disruptive event. In this paper, we address the interdependent network restoration problem from community structures perspective. That is, how to restore community structures in a set of infrastructure networks, that are physically interdependent, following a disruptive event. Accordingly, we develop an optimization model using mixed integer programming that aims to enhance the resilience of the set of infrastructure networks considering their physical interdependency. The model prioritizes the restoration tasks for each infrastructure network, according to their influence on the performance of their respected networks, and allocates and schedules the selected restoration tasks to the available work crews. Furthermore, we present a numerical experiment for the developed optimization model with a real set of interdependent infrastructure networks in Shelby County, TN, USA, considering multiple hypothetical earthquake scenarios of different magnitudes.

An analytical approach to the technical and economical optimization of CO2 sequestration in saline aquifers with simultaneous brine production

In the course of developing innovative approaches for climate change mitigation, CO2 sequestration in saline aquifers has drawn attention to itself as an appealing and effective procedure. Simultaneous brine production facilitates storage capacity enhancement in aquifers through formation depressurization. This study is devoted to developing an analytical optimization model for CO2 sequestration projects with simultaneous brine production from a technical and economical perspective. While previous studies have provided analytical optimization models for simple CO2 injection scenarios, this research aims to improve the former models by utilizing more precise transient pressure calculation formulations, up-to-date economic analysis and extending the models to also account for simultaneous brine production in CO2 storage projects. Through this model, the effect of different design parameters including well spacing, the number of injection and production wells, production-to-injection ratio, and carbon revenue can be assessed on the project's storage capacity and net revenue. Subsequently, the design parameters for the most environmentally and financially beneficial scenarios can be identified. The developed optimization model can serve as a fast and accurate pre-implementation tool for the case by case assessment and comparison of different CO2 sequestration project designs. In order to put the model to test, the developed multi-well pressure evolution formulation is used to predict pressure changes in a synthetic aquifer formation throughout several sequestration projects. This profile is then compared with numerical simulation results. The maximum CO2 injection rate and thus storage capacity for numerous project designs is then calculated based on a correlation between a reference injection rate, the time-dependant pressure profile for this rate and the aquifer's formation fracture pressure. Finally the effect of different design parameters is assessed on the storage capacity and net revenue and the fundamentals of selecting the best project design is discussed.

A Developed Optimization Model for Mass Production Scheduling Considering the Role of Waste Materials

A Developed Optimization Model for Mass Production Scheduling Considering the Role of Waste Materials

Comparative characteristics of the land use structure for different types of territorial communities

Purpose. The assess and compare the structure of land use of urban, township and rural territorial communities, to develop an optimization model to land use in case of decentralization Methods. Assessment, comparative-geographical, geoinformation, statistical, mathematical, geoecological analysis and optimization modeling. During the study, special methods were used to determine the anthropogenic pressure, coefficients of ecological stability and anthropogenic transformation of different types territorial communities. Results. According to the results of comparative assessment of the land use structure of urban, township and rural territorial communities, it was found that all three types of communities are characterized by a high degree of plowing (˃60%) and low forest share (5-10%). The share of natural lands within the studied territorial communities is 23-30%. The calculated coefficients of anthropogenic transformation, ecological stability, anthropogenic load, showed that Vyshnivets township and Borsukivska rural territorial communities belong to the category of ecologically unstable territories, Lanovets urban territorial community - to medium ecologically stable territories. In all three types of territorial communities, landscapes are moderately transformed. Urban, township and rural territorial communities experience a sufficiently high (above average) anthropogenic pressure. The obtained results of calculations create objective preconditions for optimizing the structure of land use of the studied territorial communities. Conclusions. To correct and improve the situation, to bring the territories of the studied territorial communities of different types to ecological stability, it is necessary to implement a number of optimization measures. The developed optimization model of land use of Lanovets city, Vyshnivets township and Borsukivka rural territorial communities envisages reduction of arable lands by 20-26%, increase of forest cover by 13-18% and bringing the share of natural lands to the optimal 50%. The implementation of such an approach requires a change in the purpose of individual land plots and the organization of their landscape-adapted use.

A stochastic-interval model for optimal scheduling of PV-assisted multi-mode charging stations

Nowadays, photovoltaic-assisted charging stations are becoming popular worldwide because its capacity to accommodate more clean energy, reduce carbon emissions, alleviate peak charging loads and provide wider charging infrastructures worldwide. When these infrastructures are operated locally, energy management becomes a challenge due to the large number and heterogeneity of uncertainties involved. This aspect is especially noticeable in the case of charging demand, which is difficult to predict. To address this issue, this paper develops a novel stochastic-interval model for optimal scheduling of multi-mode photovoltaic-assisted charging stations. The developed model uses interval formulation to model uncertainties from photovoltaic generation and energy price, while a comprehensive stochastic model is proposed for charging demand. The developed optimal scheduling model is solved using a developed iterative model, which avoids using interval arithmetic explicitly. This methodology encompasses two Mixed-integer linear programming problems and one Quadratic-programming problem, that can be efficiently addressed by conventional solvers, and allows adopting optimistic or pessimistic strategies. A case study is presented on a benchmark mid-size charging station to validate the developed model. As a sake of example, the system profit grows by 9% and decreases by 3% adopting optimistic and pessimistic point of view, respectively. Likewise, total PV generation increases by 150 kWh/day and reduces by 50 kWh/day. Similar conclusions are extracted for other parameters like monetary balances, PV peak power or satisfied EV demand.